Resilience in Manufacturing network, MIT

VAR

1. NIKE Resilience in Manufacturing Network CTL – Capstone Project 2022-2023 Gianmarco Merino Mostafa Khedr Elzanfaly

2. Supply Chain Resilience Definition TBD

3. Proxies do not quantify SC Resilience directly, but they use some Supply Chain metrics to capture indirectly the resilience level Value at Risk

4. In this model, researchers used two mathematical models to simulate a two-stage supplier–manufacturer coordinated supply chain system and optimize the recovery plans in the transportation sector. The models also used the backlog and lost sales as indicators for the cost of disruptions to minimize using the mathematical model . Description Advantages Limitations Data Proxies Delivery Delay and Quantity loss The Model simulates the affect of disruptions and translate it to cost using the proxies. The model also optimizes the recovery plan for the simulated disruptions to minimize the cost of disruptions. The model doesn’t consider the safety stock for the manufacturer. Also, the model is limited to the affect of disruptions on transportation network only. 1. Delivery Delay and Quantity Loss Bill of material, suppliers lot size, manufacturers lot size, annual demand for manufacturers, annual demand for retailers, production rate for suppliers and manufacturers, holding costs per unit for each stage, setup time and setup costs for suppliers and manufacturers Reference Proxies to measure Supply Chain Resilience

5. In this model, the paper evaluates the efficiency of suppliers in the normal phase. And then, it builds a two-stage possibilistic-stochastic programming model to simulate partial or full disruptions and measure their effect on the supply chain network using mathematical programming and data envelopment analysis (DEA) models. Description Advantages Limitations Data Proxies Percentage of disrupted suppliers in terms of loss of capacities The model builds a resilience enhancement through optimization models for operational and disruption risks The model doesn’t consider the disruption in transportation links to reflect real cases. It only focuses on suppliers’ firms and loss of capacities during disruptions. Also, the model only considers Tier 1 suppliers. 2. Percentage of Disrupted Suppliers Performance evaluation using a fuzzy DEA model Resilience enhancement using an optimization model under operational and disruption risks The detailed network of upstream supply chain Set of suppliers and their demand. Output of each suppliers and efficiency. Set of customers/retailers and their demand Reference Proxies to measure Supply Chain Resilience

6. 3. MULTIPLE SOURCING STRATEGY Proxies Advantages Limitations Data Cycle Service Level, shortage costs, and total costs Their findings stress that, when decision-makers are risk-averse and cautious, multiple-sourcing results in better service level with a lower conditional value of risk compared to single sourcing Independent likelihood disruptions Completely reliable transportation network Focus on electronic industry Upstream Network, Flows, Cycle Service Level, Costs, VaR Description A scenario-based mathematical model under single and multiple sourcing. Reference Proxies to measure Supply Chain Resilience

7. Proxies to measure Supply Chain Resilience 4. INVENTORY POSITIONING The authors proposed a two-stage scenario based on a mixed stochastic-possibilistic programming model, where two types of inventory are taken into account: (1) additional initial production capacity of the production facility, and (2) emergency inventory of a specific product type in the distribution center. A new indicator is developed for optimizing the resilience of the supply chain quantitatively based on available capacities provided by required restorations. Also, an investment comparison is built with different strategies Independent likelihood of disruptions Supplier inventory Internal Inventory Logistic cost Upstream Network, Flows, Demand, Service Level, investment cost Reference Proxies Advantages Limitations Data Description Capacity Restoration over time

8. Proxies to measure Supply Chain Resilience 5. BACKUP SUPPLIERS A multi-objective stochastic programming model, where primary and backup suppliers are separately modeled as binary decision variables. The primary objective function of their model is to minimize the total cost of the SC under different disruption scenarios. It helps to determine outsourcing decisions and resilience strategies that can help maintain the sustainability performance of the supply chain in random disruptions. Independent likelihood of disruptions Supplier data Plastic pipe industry Constraint a Sustainability score Upstream Network, Flows, Cycle Service Level, Cost Backup suppliers, purchasing costs Reference Proxies Advantages Limitations Data Description Expected Total Cost

9. Proxies to measure Supply Chain Resilience A consumer products company has created its own automated, data-based, risk-assessment tool that considers a variety of risks. It is informed by external sources of data (e.g., monitoring services and financial reports) and internally generated sources. It helps to identify priorities for attention. Internal estimates of TTR and TTS Upstream network, Flows, External data (financial reports, monitoring services), VaR 6. RISK-ASSESSMENT TOOL Reference Proxies Advantages Limitations Data Description TTR and TTS

10. Proxies of Supply Chain Resilience 7. STRESS TEST Description Advantages Limitations Data The stress test requires creating a digital twin in the supply chain with suppliers and collect data from each supplier for the time to recover and time to survive and then build simulations of disruption models to test the resilience of the supply chain. It get accurate data of time to survive and time to recover for each node in the supply chain from historical data on disruptions and how much does it cost for any change in the network. It provides a dashboard for the current state of supply chain resilience and the cost of increasing the resilience in any part of the manufacturing network It’s complicated and requires a lot of work from suppliers to update their TTR and TTS regularly to have reliable results on the network’s resilience level TTR & TTS from each supplier in the network, Bill of materials, operational and financial measures, in-transit and on-site inventory levels, demand forecast for each product Reference

11. Proxies of Supply Chain Resilience 8. Resilience coefficient 1 It derives four indices ( evenness, resilience, continuity of supply, and climate resilience) to estimate the performance of supply chains in disruptions. The Evenness assesses how well the risk is spread throughout SC. Resilience is based on the connectedness of the SC, and climate resilience is defined as the continuity flow after a particular climate shock. Independent likelihood of disruptions Climate shocks Agricultural SC Upstream network Flows, Quantity A metric bounded between [0,1] that can be optimized. It is relatively easy to compute. Reference Proxies Advantages Limitations Data Description Connectedness of SC and Flow Continuity

12. Proxies of Supply Chain Resilience 9. Resilience coefficient 2 A metric bounded between [0,1] that can be optimized. A supply resilience objective function to calculate the resilience level of the selected supply base and considering several strategies such as suppliers’ business continuity plans, fortification of suppliers and contract with backup suppliers to enhance the resilience level of the supply network Upstream network, Flows, Quantities, TTR https://www.sciencedirect.com/science/article/abs/pii/S136655451500071X Independent likelihood of disruptions Natural disasters RE: resilience RE’: lost of resi lience A: lost capacity recovered by inventory prepositioning B: lost capacity recovered by backup supplier C: lost capacity recovered by restoration of disrupted supplier LTa , LTb , LTc : denote the time of receiving items associated with the A , B, and C Proxies Advantages Limitations Data Description Capacity recovery rate by firm’s inventory, backup suppliers, and current suppliers

13. Proposed Model

14. Model of Supply Chain Resilience A metric bounded between [0,1] that can be optimized. Several constraints, according to the data available Cost of allocation, impact on sales A new upstream supply chain resilience objective function to calculate the resilience level in terms of the recovery of cycle service level after disruptions. The model considers several aspects such as suppliers’ inventory, backup suppliers, internal inventory, cost of allocation and materials. The model estimates the resilience level of the company with different investments scenarios. Upstream network, Flows, Quantities, Cost of allocation, Cost of products Independent likelihood of disruptions Based on stochastic scenarios Proxies Advantages Limitations Data Description Cycle Service Level and total cost Proposed Model

15. Definition Data Modeling Testing Takeaways Detailed definition and description of the mixed framework Develop simulated data of an upstream supply network Test the framework with different strategies in the network Highlight the main takeaways of different observations

16. Product 1 (100%) C1 C2 P1 M1 M2 M3 M4 Component 1 (65%) Component 2 (35%)

17. 1 2 3 1 node In =   Out = = 0.25  

18. TIER 1 C1 C2 P1 M1 M2 M3 M4 TIER 2 TIER 3 TIER 4

19. Product 1 (100%) Component 1 ( 40 % ) Component 2 ( 30 % ) Component 3 ( 30 % ) Material 1 ( 100 % ) Material 2 ( 100 % ) Material 3 ( 100 % )

20. P1-1 DC Tier 1 Finished Goods Factories Tier 2 FG Component Suppliers Tier 3 Material Suppliers M2-1 M1-1 M3-1 M2-2 M1-2 M3-2 C1-1 C2-1 C3-1 C1-2 C2-2 C3-2 P1-2 SINGLE

21. P1-1 DC Tier 1 Finished Goods Factories Tier 2 FG Component Suppliers Tier 3 Material Suppliers M2-1 M1-1 M3-1 M2-2 M3-2 C1-1 C1-2 C2-1 C3-1 C3-2 C3-3 P1-2 MIXED %8 %8 %20 %20 %12 %15 %15 %30 %11 %11 %15 %38 %19

22. Tier 2 FG Component Suppliers MIXED Tier 3 Material Suppliers Tier 1 Finished Goods Factories

23. OLD

24. P1-1 DC Tier 1 Finished Goods Factories Tier 2 FG Component Suppliers Tier 3 Material Suppliers M2-1 M1-1 M3-1 M2-2 M1-2 M3-2 C1-1 C2-1 C3-1 C1-2 C2-2 C3-2 P1-2 SINGLE %14 %14 %14 %14 %14 %14 %14 %14 %14 %14 %14 %14 %14 %14 %28 %28

25. P1-1 DC Tier 1 Finished Goods Factories Tier 2 FG Component Suppliers Tier 3 Material Suppliers M2-1 M1-1 M3-1 M2-2 M1-2 M3-2 C1-1 C2-1 C3-1 C1-2 C2-2 C3-2 P1-2 DUAL BACKUP M2-1B M1-1B M3-1B M2-2 M1-2B M3-2B

26. P1-1 DC Tier 1 Finished Goods Factories Tier 2 FG Component Suppliers Tier 3 Material Suppliers M2-1 M1-1 M3-1 M2-2 M1-2 M3-2 C1-1 C2-1 C3-1 C1-2 C2-2 C3-2 P1-2 DUAL BACKUP M2-1B M1-1B M3-1B M2-2 M1-2B M3-2B %5 %5 %5 %5 %5 %5 %5 %5 %5 %5 %5 %5 %7 %7 %7 %7 %7 %7 %21 %21

27. P1-1 DC Tier 1 Finished Goods Factories Tier 2 FG Component Suppliers Tier 3 Material Suppliers M2-1 M1-1 M3-1 M2-2 M3-2 C1-1 C1-2 C2-1 C3-1 C3-2 C3-3 P1-2 MIXED

28. P1-1 DC Tier 1 Finished Goods Factories Tier 2 FG Component Suppliers Tier 3 Material Suppliers M2-1 M1-1 M3-1 M2-2 M3-2 C1-1 C1-2 C2-1 C3-1 C3-2 C3-3 P1-2 MIXED %8 %8 %20 %20 %12 %15 %15 %30 %11 %11 %15 %38 %19